{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe70a914-ef34-4f2a-afc5-69a6d1567aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44d6a9f1-680c-4785-aefe-28e42aede486",
   "metadata": {},
   "source": [
    "# 合并(merge)数据框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a8239fd-bc64-4e3b-88ef-362f4796bd99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>课程</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>高数</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>线代</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>英语</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   课程  学分\n",
       "0  高数   6\n",
       "1  线代   4\n",
       "2  英语   3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩\n",
       "0  张三  高数  91\n",
       "1  张三  线代  88\n",
       "2  李四  高数  78\n",
       "3  王五  马哲  95"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "课程信息 = pd.DataFrame({\n",
    "    '课程': ['高数', '线代', '英语'],\n",
    "    '学分': [6, 4, 3]\n",
    "})\n",
    "成绩 = pd.DataFrame({\n",
    "    '姓名': ['张三', '张三', '李四', '王五'],\n",
    "    '课程': ['高数', '线代', '高数', '马哲'],\n",
    "    '成绩': [91, 88, 78, 95]\n",
    "})\n",
    "\n",
    "display(课程信息); display(成绩)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20fadd8a-0138-4fb1-933e-9f3b803784b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "1  李四  高数  78   6\n",
       "2  张三  线代  88   4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, on='课程')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37fe9e61-20ee-4f50-9748-1b04ce21ebc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f07f28c8-53fc-4edb-aa61-d067d5eb2d31",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "多对多的情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "683ad7c3-0b27-4dc1-95a4-34d2b2ceed76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "1  张三  高数  91   3\n",
       "2  李四  高数  78   6\n",
       "3  李四  高数  78   3\n",
       "4  张三  线代  88   4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "课程信息2 = pd.DataFrame({\n",
    "    '课程': ['高数', '线代', '高数'],\n",
    "    '学分': [6, 4, 3]\n",
    "})\n",
    "\n",
    "成绩.merge(课程信息2, on='课程')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d64526-0ac3-4845-be5e-ce97313ab452",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "b4d9916d-775d-4035-a5fb-9324626a548f.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d0dbf065-04b1-4ba9-aa78-4af4fdb84d11",
   "metadata": {},
   "source": [
    "# 合并数据的连接类型\n",
    "\n",
    "<img src=\"attachment:b4d9916d-775d-4035-a5fb-9324626a548f.png\" style=\"width:800px\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "291639e7-0a43-467e-9d2d-08b3dd1e891a",
   "metadata": {},
   "source": [
    "### 内连接(inner join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9907cb5-899d-4919-bda7-f0926de4b85a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "1  李四  高数  78   6\n",
       "2  张三  线代  88   4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, on='课程')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25bf0a8a-38f1-44a0-9019-846af25fad2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "1  李四  高数  78   6\n",
       "2  张三  线代  88   4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, how='inner', on='课程')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5c3bd96-275b-4619-b3a1-05118206f6ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "1  李四  高数  78   6\n",
       "2  张三  线代  88   4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63af36c2-7dd4-40f8-a08f-9ad867ac458e",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "markdown",
   "id": "4dc39cfc-7503-477b-9478-2d05b3b99e62",
   "metadata": {},
   "source": [
    "### 左连接(left join)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "39b5c378-d808-42d3-9d53-afded0a199ff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>课程</th>\n",
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       "      <th>学分</th>\n",
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       "  </thead>\n",
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       "      <td>张三</td>\n",
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       "      <td>88</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩   学分\n",
       "0  张三  高数  91  6.0\n",
       "1  张三  线代  88  4.0\n",
       "2  李四  高数  78  6.0\n",
       "3  王五  马哲  95  NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, how=\"left\", on=\"课程\")"
   ]
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  {
   "cell_type": "markdown",
   "id": "f12c079b-eb9c-4920-aeb1-0ebedb15e664",
   "metadata": {},
   "source": [
    "### 右连接(right join)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec558e32-952a-4e45-acf2-d1798b38fdbd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
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       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>张三</td>\n",
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       "      <td>91.0</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>英语</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    姓名  课程    成绩  学分\n",
       "0   张三  高数  91.0   6\n",
       "1   李四  高数  78.0   6\n",
       "2   张三  线代  88.0   4\n",
       "3  NaN  英语   NaN   3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, how=\"right\", on=\"课程\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65fcdee7-bff3-4acb-9b46-7177a39d0464",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 外连接(outer join)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e910fdbe-d30d-49e2-90ed-004f12597fff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
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       "      <th>学分</th>\n",
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>英语</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
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       "</table>\n",
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      "text/plain": [
       "    姓名  课程    成绩   学分\n",
       "0   张三  高数  91.0  6.0\n",
       "1   李四  高数  78.0  6.0\n",
       "2   张三  线代  88.0  4.0\n",
       "3   王五  马哲  95.0  NaN\n",
       "4  NaN  英语   NaN  3.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(课程信息, how=\"outer\", on=\"课程\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61712323-5681-4cc8-8604-d976c6498526",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "91d6ea10-e3be-4f60-80ec-96977e4acb34",
   "metadata": {},
   "source": [
    "\n",
    "### 左右两侧具有相同的列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "57eb8437-5325-4b53-bbed-c052901e572c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>籍贯</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>山东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>天津</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  籍贯\n",
       "0  张三  山东\n",
       "1  李四  北京\n",
       "2  王五  天津"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>籍贯</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>山东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  籍贯\n",
       "0  张三  山东\n",
       "1  李四  北京"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1  = pd.DataFrame({\n",
    "    '姓名': ['张三', '李四', '王五'],\n",
    "    '籍贯': ['山东', '北京', '天津']\n",
    "})\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    '姓名': ['张三', '李四'],\n",
    "    '籍贯': ['山东', '北京']\n",
    "})\n",
    "\n",
    "display(df1); display(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac68f5e1-ef09-4d47-95d6-645625b26701",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>籍贯_x</th>\n",
       "      <th>籍贯_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>山东</td>\n",
       "      <td>山东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>天津</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名 籍贯_x 籍贯_y\n",
       "0  张三   山东   山东\n",
       "1  李四   北京   北京\n",
       "2  王五   天津  NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how=\"outer\", on=\"姓名\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28945cf1-9c19-4290-b937-03bfbc191586",
   "metadata": {},
   "source": [
    "使用参数`suffixes`指明左右两侧的相同名字附加后缀的内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "724fb5fc-cede-4559-be1f-c9c1e8d62007",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>籍贯_df1</th>\n",
       "      <th>籍贯_df2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>山东</td>\n",
       "      <td>山东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>天津</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名 籍贯_df1 籍贯_df2\n",
       "0  张三     山东     山东\n",
       "1  李四     北京     北京\n",
       "2  王五     天津    NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how=\"outer\", on=\"姓名\", suffixes=('_df1','_df2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2efc5498-3cfb-4d91-a422-3041d14e007d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e4fb179a-7749-4552-ad62-2e705ef60927",
   "metadata": {},
   "source": [
    "### 使用行索引标签作为连接条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dac64107-8e85-4a86-8c62-91ae7f0c658f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>课程</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>高数</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>线代</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>英语</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    学分\n",
       "课程    \n",
       "高数   6\n",
       "线代   4\n",
       "英语   3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = 课程信息.set_index('课程')\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a143c2a-3dbb-44b2-869a-020755afaba2",
   "metadata": {},
   "source": [
    "使用`left_index`、`right_index`、`left_on`和`right_on`指明哪侧使用index(行索引)、哪侧使用是列作为左右连接的条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a9fff68-24c0-4ce5-87b9-e733a58f5419",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  高数  91   6\n",
       "2  李四  高数  78   6\n",
       "1  张三  线代  88   4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "成绩.merge(df2, how=\"inner\", right_index = True, left_on=\"课程\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0717d4a-2cb7-4be7-a33f-cc0a5f2a908d",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "# 拼接(concatenate)数据框"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d735ddd4-52b5-4d1d-8cc6-13f406a8163a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
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       "      <td>88</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "   姓名  课程  成绩\n",
       "0  张三  高数  91\n",
       "1  张三  线代  88"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
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       "      <th>学分</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>张三</td>\n",
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       "      <td>88</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
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       "      <td>3</td>\n",
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      "text/plain": [
       "   姓名  课程  成绩  学分\n",
       "0  张三  线代  88   4\n",
       "1  李四  高数  78   6\n",
       "2  王五  马哲  95   3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "成绩1 = pd.DataFrame({\n",
    "    '姓名': ['张三', '张三'],\n",
    "    '课程': ['高数', '线代'],\n",
    "    '成绩': [91, 88]\n",
    "})\n",
    "\n",
    "成绩2 = pd.DataFrame({\n",
    "    '姓名': ['张三', '李四', '王五'],\n",
    "    '课程': ['线代', '高数', '马哲'],\n",
    "    '成绩': [88, 78, 95],\n",
    "    '学分': [4, 6, 3]\n",
    "})\n",
    "\n",
    "display(成绩1); display(成绩2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36807556-33be-4f54-b7eb-b2da8d5c85e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
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       "      <td>88</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
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       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩   学分\n",
       "0  张三  高数  91  NaN\n",
       "1  张三  线代  88  NaN\n",
       "0  张三  线代  88  4.0\n",
       "1  李四  高数  78  6.0\n",
       "2  王五  马哲  95  3.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([成绩1, 成绩2])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d36873e-75e5-486f-9e02-2ba718783d7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
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       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
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      "text/plain": [
       "   姓名  课程  成绩   学分\n",
       "1  张三  线代  88  NaN\n",
       "1  李四  高数  78  6.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   "source": [
    "df3.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09505590-e37f-4a28-883a-fb9716f6b4d7",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a51c03b6-ef3d-4a7e-8c06-4acb79882c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>张三</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩   学分\n",
       "0  张三  高数  91  NaN\n",
       "1  张三  线代  88  NaN\n",
       "2  张三  线代  88  4.0\n",
       "3  李四  高数  78  6.0\n",
       "4  王五  马哲  95  3.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([成绩1, 成绩2], ignore_index=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d4decb48-a6ce-48d1-9978-5bff5e480f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>姓名</th>\n",
       "      <th>课程</th>\n",
       "      <th>成绩</th>\n",
       "      <th>学分</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>张三</td>\n",
       "      <td>高数</td>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>张三</td>\n",
       "      <td>线代</td>\n",
       "      <td>88</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>李四</td>\n",
       "      <td>高数</td>\n",
       "      <td>78</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>王五</td>\n",
       "      <td>马哲</td>\n",
       "      <td>95</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   姓名  课程  成绩   学分\n",
       "0  张三  高数  91  NaN\n",
       "1  张三  线代  88  NaN\n",
       "2  张三  线代  88  4.0\n",
       "3  李四  高数  78  6.0\n",
       "4  王五  马哲  95  3.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.concat([成绩1, 成绩2]).reset_index(drop=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37d9ff09-c05c-4944-b366-ae3b4b3c4c64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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       "张三  线代    2\n",
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       "   姓名  课程  成绩   学分\n",
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       "   姓名  课程  成绩   学分\n",
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       "    姓名   课程    成绩  姓名  课程  成绩  学分\n",
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       "         成绩    成绩   学分\n",
       "姓名 课程                 \n",
       "张三 高数  91.0   NaN  NaN\n",
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